Instructions to use google/gemma-3-27b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/gemma-3-27b-it with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="google/gemma-3-27b-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("google/gemma-3-27b-it")
model = AutoModelForImageTextToText.from_pretrained("google/gemma-3-27b-it")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use google/gemma-3-27b-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "google/gemma-3-27b-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-3-27b-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/google/gemma-3-27b-it

SGLang

How to use google/gemma-3-27b-it with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "google/gemma-3-27b-it" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-3-27b-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "google/gemma-3-27b-it" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "google/gemma-3-27b-it",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use google/gemma-3-27b-it with Docker Model Runner:
```
docker model run hf.co/google/gemma-3-27b-it
```

Fail to Load Gemme3 27B

#14

by crm-ai - opened Mar 13, 2025

Discussion

crm-ai

Mar 13, 2025

Code:

    config = AutoConfig.from_pretrained(base_model_name)
    for key, value in vars(config.text_config).items():
        setattr(config, key, value)
    config.use_cache=False

    #print(f"config: {config}")

    device_map = (
        {"": f"xpu:{Accelerator().local_process_index}"}
        if is_xpu_available()
        else {"": Accelerator().local_process_index}
    )

    base_model = AutoModelForCausalLM.from_pretrained(
        base_model_name,
        config=config,
        quantization_config=quantization_config,
        use_flash_attention_2=True,
        torch_dtype=torch.bfloat16,
        device_map = "cpu"
    )

Error:

Traceback (most recent call last):
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 582, in <module>
    main()
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 529, in main
    tokenizer, policy_model = initialize_tokenizer_and_model()
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 508, in initialize_tokenizer_and_model
    base_model = AutoModelForCausalLM.from_pretrained(
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
    return model_class.from_pretrained(
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 273, in _wrapper
    return func(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4531, in from_pretrained
    dispatch_model(model, **device_map_kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/accelerate/big_modeling.py", line 501, in dispatch_model
    model.to(device)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3263, in to
    return super().to(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1152, in to
    return self._apply(convert)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 825, in _apply
    param_applied = fn(param)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1150, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
NotImplementedError: Cannot copy out of meta tensor; no data!
Traceback (most recent call last):
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 582, in <module>
    main()
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 529, in main
    tokenizer, policy_model = initialize_tokenizer_and_model()
  File "/home/ec2-user/SageMaker/zhichao/Gemma3-27B/UNA_trainer_score_MSE_1e-4_0.01.py", line 508, in initialize_tokenizer_and_model
    base_model = AutoModelForCausalLM.from_pretrained(
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
    return model_class.from_pretrained(
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 273, in _wrapper
    return func(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4531, in from_pretrained
    dispatch_model(model, **device_map_kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/accelerate/big_modeling.py", line 501, in dispatch_model
    model.to(device)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3263, in to
    return super().to(*args, **kwargs)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1152, in to
    return self._apply(convert)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 825, in _apply
    param_applied = fn(param)
  File "/home/ec2-user/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1150, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
NotImplementedError: Cannot copy out of meta tensor; no data!

graftim

Mar 17, 2025

•

edited Mar 17, 2025

I get the same error. Do you use accelerate to launch it?

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